hectorLop/Conditional-Adversarial-Domain-Generalization-with-Single-Discriminator

Pytorch implementation of the paper: "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnosis"

37
/ 100
Emerging

This tool helps maintenance engineers and operations managers diagnose bearing faults in machinery, even when the data comes from different operating conditions or sensor setups. You provide historical vibration data and corresponding fault labels, and it produces a model that can identify bearing issues like inner race, outer race, or roller faults across various operational environments. This is particularly useful for industrial settings where machines operate under diverse and changing conditions.

No commits in the last 6 months.

Use this if you need to build a robust fault diagnosis system for industrial bearings that can generalize well to new, unseen operating conditions without needing to retrain a new model for each specific scenario.

Not ideal if you are looking for a general-purpose anomaly detection tool or if your fault diagnosis problem does not involve domain shift challenges across different operational environments.

predictive-maintenance fault-diagnosis industrial-machinery condition-monitoring operations-management
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 14 / 25

How are scores calculated?

Stars

40

Forks

6

Language

Python

License

MIT

Last pushed

Jan 13, 2022

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/hectorLop/Conditional-Adversarial-Domain-Generalization-with-Single-Discriminator"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.